Adaptive predator–prey optimization for tuning of infinite horizon LQR applied to vehicle suspension system

Rashmi Ranjan Das, Vinodh Kumar Elumalai, Raaja Ganapathy Subramanian, Kadiyam Venkata Ashok Kumar

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)

Abstract

This paper puts forward an adaptive predator–prey optimization algorithm to solve the weight selection problem of linear quadratic control applied for vibration control of vehicle suspension system. The proposed technique addresses the two key issues of PSO, namely (a) the premature convergence of the particles, and (b) the imbalance between exploration and exploitation of the particles in finding the global optimum. The main principle behind this optimization algorithm is that the inertia weight is adaptively updated based on the success rate of the particles to increase the convergence, and the predator–prey strategy is reinforced to avoid the particles getting trapped in a local minimum thereby, guaranteeing convergence of the particles towards the global optimal solution. The convergence of the particles towards the global minimum is guaranteed on the basis of a passivity argument. Moreover, the strength of this new adaptive optimization technique to tune the gains of linear quadratic regulator is validated experimentally on a laboratory scale active vehicle suspension system for improved ride comfort and passenger safety.

Original languageEnglish
Pages (from-to)518-526
Number of pages9
JournalApplied Soft Computing
Volume72
DOIs
Publication statusPublished - 1 Nov 2018

Keywords

  • Active vehicle suspension system
  • AIWF
  • LQR
  • Predator–prey strategy
  • PSO

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